Move from AI experiments to AI in production.
Use-case portfolios, shared AI platforms, governance and production delivery for organisations adopting AI at scale.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Most organisations have AI pilots; few have AI systems that are trusted, measured and embedded in how work gets done. The gap is engineering, data and governance — not model availability.
Shivacha AI transformation builds the capability to deliver AI repeatedly: prioritised use-case portfolios, a shared platform for model access, retrieval and evaluation, practical governance, and production launches of assistants, agents and ML models with measured outcomes.
Challenges
What stands in the way
Pilot purgatory
Prototypes that never reach production.
Trust and accuracy
Users unwilling to rely on AI outputs.
Data readiness
Knowledge and data not accessible or governed.
Risk concerns
Security, privacy and model risk unresolved.
Approach
How Shivacha helps
- 1
Portfolio, not projects
Balanced use cases with owners and metrics.
- 2
Shared platform
Gateway, retrieval and evaluation reused across use cases.
- 3
Evaluation-driven
Quality measured before and after every change.
- 4
Governed autonomy
AI autonomy expanded as reliability is proven.
Deliverables
- AI opportunity assessment
- Use-case roadmap
- Enterprise AI platform
- Production assistants and agents
- AI governance framework
- Training and enablement
Services
Services in this solution
Enterprise AI Solutions
Enterprise AI programmes: shared AI platforms, governance, security and a portfolio of production use cases across the organisation.
Learn moreAI Consulting
Practical AI consulting from engineers: use-case prioritisation, architecture, build-vs-buy, vendor selection and roadmaps.
Learn moreAI Agent Development
AI agents that plan, use tools and complete multi-step business tasks — with scoped permissions, approvals and full audit trails.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreMachine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreProducts
Platforms that accelerate delivery
Shivacha Agent Platform
Build, govern and operate AI agents that do real work across your systems.
Learn moreShivacha Copilot Kit
Embed a context-aware AI copilot into your product or internal tools.
Learn moreShivacha AI Analytics
Ask questions of your data in plain language — with governed, verifiable answers.
Learn moreShivacha Document AI
Extract, validate and route data from any business document.
Learn moreLearn more
Related work and thinking
Permission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreAgentic claims intake with human approval
A reference design for an AI workflow that reads claim submissions, extracts and validates data, and prepares cases for adjusters.
Learn moreWhy most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreDesigning AI products users trust
Trust in AI products is designed: show sources, make uncertainty visible, make correction easy and keep humans in control of consequential actions.
Learn moreEnterprise AI Architecture Guide
A practical blueprint for the shared AI platform — model gateway, retrieval, evaluation, governance — that lets enterprises deliver AI use cases repeatedly.
Learn moreFAQ
Frequently asked questions
Where should AI transformation start?
With two or three high-value use cases on shared foundations, delivered to production with measured outcomes.
Do you provide AI governance?
We implement practical governance in tooling and process, aligned with your risk functions; we do not provide legal advice.
Build Your AI System.
Tell us what you're launching. A solution architect will reply with an approach, an implementation timeline and next steps.